On ensemble‐based coupled data assimilation, cross‐component parameter estimation (CPE), has not been as extensively developed and applied as weakly coupled state and parameter estimation along with cross‐component state estimation. This discrepancy is partially attributed to the lack of emphasis on the instantaneous response of coupled model states with respect to parameters across different components. We define so‐called response as the instantaneous parameter sensitivity (IPS). Under the framework of sequential assimilation, the prior information heavily relies on the IPS of coupled states with different time scales. Based on the IPS analysis for an intermediate coupled model, a series of twin experiments of state and parameter estimation are conducted, in which an IPS‐inspired adaptive inflation scheme for parameter ensemble is introduced. Results show that the success of a parameter estimation strategy is closely tied to the significant IPS of the observed state to the parameter targeted for optimization, as it maintains a high signal‐to‐noise ratio in the error covariance between parameter and prior state, thereby enhancing parameter estimation. An interesting finding in the context of IPS‐based CPE is: an atmospheric parameter can be successfully estimated by assimilating observations from slow‐varying oceanic component, but not vice versa. In comparison with cross‐component state estimation, successful CPE significantly enhances the estimation accuracy of coupled states by mitigating model bias.

This study introduces two distinct post-processing strategies to address systematic biases in sea surface temperature (SST) numerical forecasts, thereby enhancing SST predictive accuracy. The first strategy implements a spatiotemporal four-dimensional multi-grid analysis (4D-MGA) scheme within a three-dimensional variational (3D-Var) data assimilation framework. The second strategy establishes a hybrid deep learning architecture integrating empirical orthogonal function (EOF) analysis, empirical mode decomposition (EMD), and a backpropagation (BP) neural network (designated as EE–BP). The 4D-MGA strategy dynamically corrects systematic biases through a temporally coherent extrapolation of analysis increments, leveraging its inherent capability to characterize intrinsic temporal correlations in model error evolution. In contrast, the EE–BP strategy develops a bias correction model by learning the systematic biases of the SST numerical forecasts. Utilizing a satellite fusion SST dataset, this study conducted bias correction experiments that specifically addressed the daily SST numerical forecasts with 7-day lead times in the Kuroshio region south of Japan during 2017, systematically quantifying the respective error reduction potentials of both strategies. Quantitative verification reveals that EE–BP delivers enhanced predictive skill across all forecast horizons, achieving 18.1–22.7% root–mean–square error reduction compared to 1.2–9.1% attained by 4D-MGA. This demonstrates deep learning’s unique advantage in capturing nonlinear bias evolution patterns.

The 4-dimensional multigrid analysis (4D-MGA) data assimilation system was developed for the 1/12° global Mass Conservation Ocean Model (MaCOM). 4D-MGA is a variational data assimilation method that accounts for error evolution across concurrent spatial and temporal scales. Dynamic height integration was incorporated into the cost function, thereby imposing a physical constraint for assimilating sea level anomaly observations. To evaluate the 4D-MGA system, sequential data assimilation was performed from 2024 April 5 to May 9. The analysis fields show that 4D-MGA effectively reduces large-scale model biases and improves the spatial representation of mesoscale eddies and surface currents. Both the root mean square error (RMSE) and bias were substantially reduced, and the sea surface height and current fields show substantially improved agreement with satellite observations. Forecasts initialized from the 4D-MGA analysis fields demonstrate consistent and moderate improvements in accuracy. The 1- and 7-d forecasts exhibit lower RMSE and bias than the free-run simulation and maintain stable performance. An intercomparison based on the GODAE Intercomparison and Validation Task Team metrics was conducted. The results place MaCOM with 4D-MGA above the average of the systems compared. MaCOM demonstrates relatively low RMSE and greater stability in salinity, sea surface temperature, and sea level anomaly. Although temperature forecasts rank below average during the first 3 d, they improve to a moderate level over the final 3 d. However, given the short duration of the current experiment, longer-term evaluation is required to fully assess the system’s sustained assimilation performance.

Using MODIS-Aqua satellite observations, this study analyzes the spatiotemporal distribution characteristics of particulate organic carbon (POC) in China’s marginal seas from 2003 to 2024. The statistical relationships between various marine environmental variables, including sea surface temperature (SST), nutrients, and primary production (PP), and POC concentrations are explored using partial least squares path modeling (PLS-PM). Finally, a box model approach is conducted to assess the POC budget in the study area. The results indicate that the POC concentration in the marginal seas of China generally exhibits a characteristic of being high in spring and low in summer. The highest concentration of POC is observed in the Bohai Sea, followed by the Yellow Sea, and the lowest in the East China Sea, with coastal waters exhibiting higher POC concentrations compared to the central areas. The spatial distribution and seasonal changes in POC are jointly influenced by PP, water mass exchange, resuspended sediments, and terrestrial inputs. Large-scale climate modes show statistical associations with POC concentration in the open waters of China’s marginal seas. PP and respiratory consumption are identified as the predominant input and output fluxes, respectively, in China’s marginal seas. This study enriches the understanding of carbon cycling processes and carbon sink mechanisms in marginal seas.

Coupled climate models integrate atmospheric, oceanic, and land submodels, while the uncertainty of model parameters from different parameterization schemes or empirically derived parameters inevitably introduces systematic biases. Coupled parameter optimization (CPO) can reduce these biases to improve weather forecast and climate prediction, but must address strong nonlinearities inherent in coupled models. The analytical four-dimensional ensemble variational (A-4DEnVar) data assimilation method retains the nonlinear processing capability of the four-dimensional variational (4D-Var) data assimilation method but gets rid of the dependence on the adjoint model. In this study, a novel dynamic independent point (DIP) scheme is introduced to the improved A-4DEnVar, which reduces computational dimensionality and further explores a broader parameter space of dimensionality reduction through the outer loop. Based on the improved A-4DEnVar, a series of geographic-dependent CPO experiments with an idealized 2D coupled model are carried out. Results show that A-4DEnVar accurately captures the geographical characteristics of parameters and effectively optimizes cross-component parameters despite strong nonlinearity. Additionally, the DIP scheme presents significant advantages compared to the static independent point scheme, especially with fewer independent points. This work is offering a new perspective for parameter optimization in coupled general circulation models used for climate estimation and prediction.

Advances in data assimilation (DA) methods have greatly improved the accuracy of Earth system predictions. To fuse multi-source data and reconstruct the nonlinear evolution missing from observations, geoscientists are developing future-oriented DA methods. In this paper, we redesign a purely data-driven latent space DA framework (DeepDA) that employs a generative artificial intelligence model to capture the nonlinear evolution in sea surface temperature. Under variational constraints, DeepDA embedded with nonlinear features can effectively fuse heterogeneous data. The results show that DeepDA remains highly stable in capturing and generating nonlinear evolutions even when a large amount of observational information is missing. It can be found that when only 10% of the observation information is available, the error increase of DeepDA does not exceed 40%. Furthermore, DeepDA has been shown to be robust in the fusion of real observations and ensemble simulations. In particular, this paper provides a mechanism analysis of the nonlinear evolution generated by DeepDA from the perspective of physical patterns, which reveals the inherent explainability of our DL model in capturing multi-scale ocean signals.